Article(id=1242136599912325540, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242136577695097200, articleNumber=null, orderNo=15, doi=10.3981/j.issn.1000-7857.2017.24.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1509552000000, receivedDateStr=2017-11-02, revisedDate=1511452800000, revisedDateStr=2017-11-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1514529964790, onlineDateStr=2017-12-29, pubDate=1514390400000, pubDateStr=2017-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1514529964790, onlineIssueDateStr=2017-12-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078043198, creator=sys-migrate, updateTime=1774078043198, updator=sys-migrate, issue=Issue{id=1242136577695097200, tenantId=1146029695717560320, journalId=1146031591421210625, year='2017', volume='35', issue='24', pageStart='1', pageEnd='97', issueExtLink='null', onlineDate='null', pubDate='1514390400000', pubDateStr='2017-12-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078037899, creator='sys-migrate', updateTime=1774078037899, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=66, endPage=70, ext={EN=ArticleExt(id=1242136606291866018, articleId=1242136599912325540, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=A neural network for short term load forecasting based on Sample self adapted of load characteristics clustering, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=This paper introduces the methods and the steps of predicting the power load by the BP neural network with cluster optimization in batch processing time series. Through the preconditioning of historical data, the setting of the initial clustering center and the determination of the optimal number of clusters, a clustering prediction model of the load curve is established based on the clustering results of the historical data and the relevant parameters such as the temperature, the humidity, the air pressure, the wind speed and the time (the current week). The results show that with the clustering algorithm, the related factors and the BP network adaptive rate can be comprehensively considered, while the training speed is improved, to obtain more accurate prediction results., authors=FANG Fang1 , BU Fanpeng2 , TIAN Shiming2 , QI Linhai3 , LI Xiawei4 , authorsList=FANG Fang, BU Fanpeng, TIAN Shiming, QI Linhai, LI Xiawei, authorCompany=1. State Grid Beijing Changping Electric Power Supply Company, Beijing 102200, China;1 , 卜凡鹏2 , 田世明2 , 齐林海3 , 李夏威4 , authorsList=方芳, 卜凡鹏, 田世明, 齐林海, 李夏威, authorCompany=1. 国网北京市电力公司昌平供电公司, 北京 102200;
科技导报
|研究论文
2017
, 35
(24) :
66
-70
基于负荷特性聚类的样本自适应神经网络台区短期负荷预测
全屏
方芳1 , 卜凡鹏2 , 田世明2 , 齐林海3 , 李夏威4
作者信息
1. 国网北京市电力公司昌平供电公司, 北京 102200;
通讯作者:
卜凡鹏(通信作者),工程师,研究方向为智能用电,电子信箱:bufan-peng@epri.sgcc.com.cn
作者简介:
方芳,工程师,研究方向为智能用电,电子信箱:411912023@qq.com
A neural network for short term load forecasting based on Sample self adapted of load characteristics clustering
FANG Fang1 , BU Fanpeng2 , TIAN Shiming2 , QI Linhai3 , LI Xiawei4
Affiliations
1. State Grid Beijing Changping Electric Power Supply Company, Beijing 102200, China;
出版时间: 2017-12-28
doi: 10.3981/j.issn.1000-7857.2017.24.008
文章导航
介绍了批量处理时间序列数据情况下，基于台区负荷特性聚类的样本自适应反向传播神经（BP）神经网络预测短期电力负荷的方法，通过对历史数据的预处理、初始聚类中心的设置以及最优聚类数目的确定，建立典型日负荷曲线的聚类预测模型。基于历史数据的聚类结果及待预测日的温度、湿度、气压、风速、星期等相关参数，使用BP神经网络算法得出待预测日负荷曲线预测结果。通过实例验证，基于台区负荷特性聚类的样本自适应神经网络短期负荷预测能够得到较为准确的预测结果。
聚类分析
/
人工神经网络
/
电力负荷预测
/
数据挖掘
This paper introduces the methods and the steps of predicting the power load by the BP neural network with cluster optimization in batch processing time series. Through the preconditioning of historical data, the setting of the initial clustering center and the determination of the optimal number of clusters, a clustering prediction model of the load curve is established based on the clustering results of the historical data and the relevant parameters such as the temperature, the humidity, the air pressure, the wind speed and the time (the current week). The results show that with the clustering algorithm, the related factors and the BP network adaptive rate can be comprehensively considered, while the training speed is improved, to obtain more accurate prediction results.
cluster analysis
/
neural networks
/
power load forecasting
/
data mining
方芳, 卜凡鹏, 田世明, 齐林海, 李夏威.
基于负荷特性聚类的样本自适应神经网络台区短期负荷预测.
科技导报,
2017
, 35
(24)
: 66
-70
.
DOI: 10.3981/j.issn.1000-7857.2017.24.008
FANG Fang, BU Fanpeng, TIAN Shiming, QI Linhai, LI Xiawei.
A neural network for short term load forecasting based on Sample self adapted of load characteristics clustering[J].
Science & Technology Review ,
2017
, 35
(24)
: 66
-70
.
DOI: 10.3981/j.issn.1000-7857.2017.24.008
2017年第35卷第24期
PDF下载
469
101
引用本文
BibTeX
文章信息
doi: 10.3981/j.issn.1000-7857.2017.24.008
接收时间：2017-11-02
首发时间：2017-12-29
出版时间：2017-12-28
收稿日期：2017-11-02
修回日期：2017-11-24
通讯作者:
卜凡鹏(通信作者),工程师,研究方向为智能用电,电子信箱:bufan-peng@epri.sgcc.com.cn
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2017.24.008
引用本文
BibTeX
2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
关闭全屏
BibTeX
EndNote
RefWorks
TxT
PDF下载
全屏
469
101
关闭全屏
北京市海淀区学院南路86号
010-62199257（仅加盟咨询）
qkjq@cast.org.cn
